Metanalysis of performance on men caused by (stMale)

Geiser C. Challco geiser@alumni.usp.br

Initial Variables and Loading Data

env <- "stMale"
gender <- "men"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
                "country:age","country:ed.level","country:intervention",
                "age:intervention","ed.level:intervention",
                "country:age:intervention","country:ed.level:intervention")
dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-env.gender-descriptive")
dat <- dat[!dat$study %in% to_remove, ]

leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")
## New names:
## • `` -> `...10`
leg <- leg[!leg$study %in% to_remove, ]

idx.e <- which(dat$env == env & dat$gender == gender)
idx.c <- which(dat$env == "control" & dat$gender == gender)

data <- data.frame(
  study = dat$study[idx.c],
  n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
  n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
  data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
    paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
  })
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])

Perform meta-analyses

m.cont <- metacont(
  n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
  studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
  fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance for",gender,"in",env)
)
summary(m.cont)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country”

m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random) country
## S1                              0.1020 [-0.7230; 0.9271]        6.0  Brazil
## S2                             -0.0146 [-0.7647; 0.7355]        7.2  Brazil
## S3                              0.0148 [-0.7882; 0.8177]        6.3  Brazil
## S4                             -0.8713 [-1.7827; 0.0402]        4.9  Brazil
## S5                              0.1029 [-0.4832; 0.6890]       11.8  Brazil
## S6                              0.1285 [-0.4460; 0.7029]       12.3  Brazil
## S7                              0.0559 [-0.4728; 0.5847]       14.5  Brazil
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5   China
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0  Brazil
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4  Brazil
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                    k     SMD            95%-CI tau^2 tau    Q  I^2
## country = Brazil   9 -0.0151 [-0.2014; 0.1712]     0   0 4.61 0.0%
## country = China    1  0.0335 [-0.7562; 0.8233]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.01    1  0.9058
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “age”

m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random) country
## S1                              0.1020 [-0.7230; 0.9271]        6.0  Brazil
## S2                             -0.0146 [-0.7647; 0.7355]        7.2  Brazil
## S3                              0.0148 [-0.7882; 0.8177]        6.3  Brazil
## S4                             -0.8713 [-1.7827; 0.0402]        4.9  Brazil
## S5                              0.1029 [-0.4832; 0.6890]       11.8  Brazil
## S6                              0.1285 [-0.4460; 0.7029]       12.3  Brazil
## S7                              0.0559 [-0.4728; 0.5847]       14.5  Brazil
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5   China
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0  Brazil
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4  Brazil
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                    k     SMD            95%-CI tau^2 tau    Q  I^2
## country = Brazil   9 -0.0151 [-0.2014; 0.1712]     0   0 4.61 0.0%
## country = China    1  0.0335 [-0.7562; 0.8233]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.01    1  0.9058
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “ed.level”

m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)         ed.level
## S1                              0.1020 [-0.7230; 0.9271]        6.0  upper-secundary
## S2                             -0.0146 [-0.7647; 0.7355]        7.2  upper-secundary
## S3                              0.0148 [-0.7882; 0.8177]        6.3  upper-secundary
## S4                             -0.8713 [-1.7827; 0.0402]        4.9 higher-education
## S5                              0.1029 [-0.4832; 0.6890]       11.8 higher-education
## S6                              0.1285 [-0.4460; 0.7029]       12.3 higher-education
## S7                              0.0559 [-0.4728; 0.5847]       14.5          unknown
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5          unknown
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0          unknown
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4  upper-secundary
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                               k     SMD            95%-CI  tau^2    tau    Q   I^2
## ed.level = upper-secundary    4  0.0629 [-0.0320; 0.1579]      0      0 0.09  0.0%
## ed.level = higher-education   3 -0.1053 [-1.3579; 1.1473] 0.0881 0.2968 3.75 46.7%
## ed.level = unknown            3 -0.0534 [-0.4235; 0.3166]      0      0 0.50  0.0%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.92    2  0.3833
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “intervention”

m.sg4sub <- update.meta(m.cont, subgroup = intervention, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                        intervention
## S1                             Gender-stereotype color, ranking, badges, and avatar
## S2                             Gender-stereotype color, ranking, badges, and avatar
## S3                             Gender-stereotype color, ranking, badges, and avatar
## S4                             Gender-stereotype color, ranking, badges, and avatar
## S5                             Gender-stereotype color, ranking, badges, and avatar
## S6                             Gender-stereotype color, ranking, badges, and avatar
## S7                             Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU           Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs           Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                      k     SMD            95%-CI tau^2 tau    Q  I^2
## intervention = Gender-stereotype color, rankin ...   9 -0.0330 [-0.2242; 0.1582]     0   0 4.40 0.0%
## intervention = Gender-stereotyped motivational ...   1  0.1040 [-0.4101; 0.6180]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.25    1  0.6186
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country:age”

m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)           country:age
## S1                              0.1020 [-0.7230; 0.9271]        6.0     Brazil:adolescent
## S2                             -0.0146 [-0.7647; 0.7355]        7.2     Brazil:adolescent
## S3                              0.0148 [-0.7882; 0.8177]        6.3     Brazil:adolescent
## S4                             -0.8713 [-1.7827; 0.0402]        4.9          Brazil:adult
## S5                              0.1029 [-0.4832; 0.6890]       11.8          Brazil:adult
## S6                              0.1285 [-0.4460; 0.7029]       12.3          Brazil:adult
## S7                              0.0559 [-0.4728; 0.5847]       14.5          Brazil:adult
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5  China:no-restriction
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0 Brazil:no-restriction
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4    Brazil:adolescence
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                       k     SMD            95%-CI   tau^2    tau    Q   I^2
## country:age = Brazil:adolescent       3  0.0306 [-0.1185; 0.1797]       0      0 0.04  0.0%
## country:age = Brazil:adult            4 -0.0149 [-0.5770; 0.5472] <0.0001 0.0007 3.85 22.2%
## country:age = China:no-restriction    1  0.0335 [-0.7562; 0.8233]      --     -- 0.00    --
## country:age = Brazil:no-restriction   1 -0.1973 [-0.7179; 0.3234]      --     -- 0.00    --
## country:age = Brazil:adolescence      1  0.1040 [-0.4101; 0.6180]      --     -- 0.00    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.87    4  0.9294
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country:ed.level”

m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)        country:ed.level
## S1                              0.1020 [-0.7230; 0.9271]        6.0  Brazil:upper-secundary
## S2                             -0.0146 [-0.7647; 0.7355]        7.2  Brazil:upper-secundary
## S3                              0.0148 [-0.7882; 0.8177]        6.3  Brazil:upper-secundary
## S4                             -0.8713 [-1.7827; 0.0402]        4.9 Brazil:higher-education
## S5                              0.1029 [-0.4832; 0.6890]       11.8 Brazil:higher-education
## S6                              0.1285 [-0.4460; 0.7029]       12.3 Brazil:higher-education
## S7                              0.0559 [-0.4728; 0.5847]       14.5          Brazil:unknown
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5           China:unknown
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0          Brazil:unknown
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4  Brazil:upper-secundary
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                              k     SMD            95%-CI  tau^2    tau    Q   I^2
## country:ed.level = Brazil:upper-secundary    4  0.0629 [-0.0320; 0.1579]      0      0 0.09  0.0%
## country:ed.level = Brazil:higher-education   3 -0.1053 [-1.3579; 1.1473] 0.0881 0.2968 3.75 46.7%
## country:ed.level = Brazil:unknown            2 -0.0726 [-1.6808; 1.5356]      0      0 0.45  0.0%
## country:ed.level = China:unknown             1  0.0335 [-0.7562; 0.8233]     --     -- 0.00    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.39    3  0.7072
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                       country:intervention
## S1                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7                             Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU            China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs           Brazil:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                              k     SMD            95%-CI tau^2 tau    Q  I^2
## country:intervention = Brazil:Gender-stereotype color, ...   8 -0.0386 [-0.2560; 0.1789]     0   0 4.37 0.0%
## country:intervention = China:Gender-stereotype color,  ...   1  0.0335 [-0.7562; 0.8233]    --  -- 0.00   --
## country:intervention = Brazil:Gender-stereotyped motiv ...   1  0.1040 [-0.4101; 0.6180]    --  -- 0.00   --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.28    2  0.8686
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “age:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                                   age:intervention
## S1                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3                                 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S5                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S6                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S7                                      adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU           no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs              adolescence:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                          k     SMD            95%-CI   tau^2    tau    Q
## age:intervention = adolescent:Gender-stereotype co ...   3  0.0306 [-0.1185; 0.1797]       0      0 0.04
## age:intervention = adult:Gender-stereotype color,  ...   4 -0.0149 [-0.5770; 0.5472] <0.0001 0.0007 3.85
## age:intervention = no-restriction:Gender-stereotyp ...   2 -0.1273 [-1.4749; 1.2202]       0      0 0.23
## age:intervention = adolescence:Gender-stereotyped  ...   1  0.1040 [-0.4101; 0.6180]      --     -- 0.00
##                                                          I^2
## age:intervention = adolescent:Gender-stereotype co ...  0.0%
## age:intervention = adult:Gender-stereotype color,  ... 22.2%
## age:intervention = no-restriction:Gender-stereotyp ...  0.0%
## age:intervention = adolescence:Gender-stereotyped  ...    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   2.15    3  0.5425
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “ed.level:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                                ed.level:intervention
## S1                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3                              upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6                             higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7                                      unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU                    unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017)          unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs            upper-secundary:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                               k     SMD            95%-CI  tau^2    tau    Q
## ed.level:intervention = upper-secundary:Gender-stereoty ...   3  0.0306 [-0.1185; 0.1797]      0      0 0.04
## ed.level:intervention = higher-education:Gender-stereot ...   3 -0.1053 [-1.3579; 1.1473] 0.0881 0.2968 3.75
## ed.level:intervention = unknown:Gender-stereotype color ...   3 -0.0534 [-0.4235; 0.3166]      0      0 0.50
## ed.level:intervention = upper-secundary:Gender-stereoty ...   1  0.1040 [-0.4101; 0.6180]     --     -- 0.00
##                                                               I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ...  0.0%
## ed.level:intervention = higher-education:Gender-stereot ... 46.7%
## ed.level:intervention = unknown:Gender-stereotype color ...  0.0%
## ed.level:intervention = upper-secundary:Gender-stereoty ...    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   1.11    3  0.7750
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country:age:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                                  country:age:intervention
## S1                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3                                 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7                                      Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU            China:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs              Brazil:adolescence:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                                  k     SMD            95%-CI   tau^2    tau
## country:age:intervention = Brazil:adolescent:Gender-stereo ...   3  0.0306 [-0.1185; 0.1797]       0      0
## country:age:intervention = Brazil:adult:Gender-stereotype  ...   4 -0.0149 [-0.5770; 0.5472] <0.0001 0.0007
## country:age:intervention = China:no-restriction:Gender-ste ...   1  0.0335 [-0.7562; 0.8233]      --     --
## country:age:intervention = Brazil:no-restriction:Gender-st ...   1 -0.1973 [-0.7179; 0.3234]      --     --
## country:age:intervention = Brazil:adolescence:Gender-stere ...   1  0.1040 [-0.4101; 0.6180]      --     --
##                                                                   Q   I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.04  0.0%
## country:age:intervention = Brazil:adult:Gender-stereotype  ... 3.85 22.2%
## country:age:intervention = China:no-restriction:Gender-ste ... 0.00    --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 0.00    --
## country:age:intervention = Brazil:adolescence:Gender-stere ... 0.00    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.87    4  0.9294
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Subgroup analysis by “country:ed.level:intervention”

m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)
## Review:     Performance for men in stMale
## 
##                                    SMD            95%-CI %W(random)
## S1                              0.1020 [-0.7230; 0.9271]        6.0
## S2                             -0.0146 [-0.7647; 0.7355]        7.2
## S3                              0.0148 [-0.7882; 0.8177]        6.3
## S4                             -0.8713 [-1.7827; 0.0402]        4.9
## S5                              0.1029 [-0.4832; 0.6890]       11.8
## S6                              0.1285 [-0.4460; 0.7029]       12.3
## S7                              0.0559 [-0.4728; 0.5847]       14.5
## S8: Conducted by BNU            0.0335 [-0.7562; 0.8233]        6.5
## S9: Albuquerque, et al. (2017) -0.1973 [-0.7179; 0.3234]       15.0
## S10: Only use prompt msgs       0.1040 [-0.4101; 0.6180]       15.4
##                                                                               country:ed.level:intervention
## S1                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3                              Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6                             Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7                                      Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU                     China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017)          Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs            Brazil:upper-secundary:Gender-stereotyped motivational message prompts
## 
## Number of studies combined: k = 10
## Number of observations: o = 396
## 
##                          SMD            95%-CI     t p-value
## Random effects model -0.0119 [-0.1788; 0.1549] -0.16  0.8750
## 
## Quantifying heterogeneity:
##  tau^2 = 0 [0.0000; 0.1169]; tau = 0 [0.0000; 0.3418]
##  I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
## 
## Test of heterogeneity:
##     Q d.f. p-value
##  4.63    9  0.8655
## 
## Results for subgroups (random effects model):
##                                                                       k     SMD            95%-CI  tau^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...   3  0.0306 [-0.1185; 0.1797]      0
## country:ed.level:intervention = Brazil:higher-education:Gender- ...   3 -0.1053 [-1.3579; 1.1473] 0.0881
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ...   2 -0.0726 [-1.6808; 1.5356]      0
## country:ed.level:intervention = China:unknown:Gender-stereotype ...   1  0.0335 [-0.7562; 0.8233]     --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...   1  0.1040 [-0.4101; 0.6180]     --
##                                                                        tau    Q   I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...      0 0.04  0.0%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 0.2968 3.75 46.7%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ...      0 0.45  0.0%
## country:ed.level:intervention = China:unknown:Gender-stereotype ...     -- 0.00    --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ...     -- 0.00    --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   0.91    4  0.9233
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))

Funnel Plot

m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))
## Eggers' test of the intercept 
## ============================= 
## 
##  intercept      95% CI      t    p
##     -1.335 -3.47 - 0.8 -1.225 0.26
## 
## Eggers' test does not indicate the presence of funnel plot asymmetry.
funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)